Assessing a treatment service based on a measure of trust dynamics

ABSTRACT

Techniques regarding autonomously determining an entity&#39;s susceptibility towards one or more treatment services are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise an assessment component that can determine a susceptibility disposition value that measures a susceptibility of an entity to a treatment service based on a trust disposition value. The trust disposition value can be determined based on a communication with the entity using machine learning technology.

PARTIES TO A JOINT RESEARCH AGREEMENT

The present subject matter was developed and the claimed invention wasmade by or on behalf of Boston Scientific Neuromodulation Corporationand International Business Machines Corporation, parties to a jointresearch agreement that was in effect on or before the effective filingdate of the claimed invention, and the claimed invention was made as aresult of activities undertaken within the scope of the joint researchagreement.

BACKGROUND

The subject disclosure relates to assessing an individual'ssusceptibility to one or more treatment services, and more specifically,to autonomously determining an individual's susceptibility to one ormore treatment services based on one or more trust dynamics measuredusing artificial intelligence technology.

SUMMARY

The following presents a summary to provide a basic understanding of oneor more embodiments of the invention. This summary is not intended toidentify key or critical elements, or delineate any scope of theparticular embodiments or any scope of the claims. Its sole purpose isto present concepts in a simplified form as a prelude to the moredetailed description that is presented later. In one or more embodimentsdescribed herein, systems, computer-implemented methods, apparatusesand/or computer program products that can autonomously assess anentity's susceptibility to one or more treatment services based on atrust dynamic measured using artificial intelligence technology aredescribed.

According to an embodiment, a system is provided. The system cancomprise a memory that can store computer executable components. Thesystem can also comprise a processor, operably coupled to the memory,and that can execute the computer executable components stored in thememory. The computer executable components can comprise an assessmentcomponent that can determine a susceptibility disposition value thatmeasures a susceptibility of an entity to a treatment service based on atrust disposition value. The trust disposition value can be determinedbased on a communication with the entity using machine learningtechnology.

According to an embodiment, a computer-implemented method is provided.The computer-implemented method can comprise determining, by a systemoperatively coupled to a processor, a susceptibility disposition valuethat measures a susceptibility of an entity to a treatment service basedon a trust disposition value. The trust disposition value can bedetermined based on a communication with the entity using machinelearning technology.

According to an embodiment, a computer program product for autonomouslyassessing a treatment service is provided. The computer program productcan comprise a computer readable storage medium having programinstructions embodied therewith. The program instructions can beexecutable by a processor to cause the processor to determine, by asystem operatively coupled to the processor, a susceptibilitydisposition value that measures a susceptibility of an entity to atreatment service based on a trust disposition value. The trustdisposition value can be determined based on a communication with theentity using machine learning technology.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a block diagram of an example, non-limiting systemthat can determine a trust disposition value regarding an entity inaccordance with one or more embodiments described herein.

FIG. 2 illustrates a block diagram of an example, non-limiting systemthat can determine a trust disposition value regarding an entity inaccordance with one or more embodiments described herein.

FIG. 3 illustrates a block diagram of an example, non-limiting systemthat can determine the susceptibility of one or more entities towardsone or more treatment services based on at least a trust dispositionvalue regarding the one or more entities in accordance with one or moreembodiments described herein.

FIG. 4 illustrates a block diagram of an example, non-limiting systemthat can determine the susceptibility of one or more entities towardsone or more treatment services based on at least a determined trustdisposition value and/or can distribute the one or more treatmentservices in accordance with one or more embodiments described herein.

FIG. 5 illustrates a block diagram of an example, non-limiting systemthat can determine the susceptibility of one or more entities towardsone or more treatment services based on at least a trust dispositionvalue and/or one or more medical diagnoses regarding the one or moreentities in accordance with one or more embodiments described herein.

FIG. 6 illustrates a flow diagram of an example, non-limiting methodthat can facilitate determining the susceptibility of one or moreentities towards one or more treatment services based on at least atrust disposition value regarding the one or more entities in accordancewith one or more embodiments described herein.

FIG. 7 illustrates a flow diagram of an example, non-limiting methodthat can facilitate determining the susceptibility of one or moreentities towards one or more treatment services based on at least atrust disposition value regarding the one or more entities in accordancewith one or more embodiments described herein.

FIG. 8 depicts a cloud computing environment in accordance with one ormore embodiments described herein.

FIG. 9 depicts abstraction model layers in accordance with one or moreembodiments described herein.

FIG. 10 illustrates a block diagram of an example, non-limitingoperating environment in which one or more embodiments described hereincan be facilitated.

DETAILED DESCRIPTION

The following detailed description is merely illustrative and is notintended to limit embodiments and/or application or uses of embodiments.Furthermore, there is no intention to be bound by any expressed orimplied information presented in the preceding Background or Summarysections, or in the Detailed Description section.

One or more embodiments are now described with reference to thedrawings, wherein like referenced numerals are used to refer to likeelements throughout. In the following description, for purposes ofexplanation, numerous specific details are set forth in order to providea more thorough understanding of the one or more embodiments. It isevident, however, in various cases, that the one or more embodiments canbe practiced without these specific details.

Medicines can exhibit side-effects and/or can cause adverse healthconditions to patients consuming the medicine. For example, somemedicines can induce a chemical dependency within the patient, whereinthe patient experiences a chemical reaction caused by the medicine thatencourages the patient to consume more of the medicine. In someinstances, the side-effects (e.g., induced chemical dependency) canresult in the patient using the medicine via methods, dosages, and/orfrequency not authorized by a medical professional. In such instances,treatment services exist to assist the patient in inhibiting theside-effect (e.g., inhibit the chemical dependency and thereby theimproper consumption of medicine). However, conventional techniques seekto distribute the treatment services in response to identifying that thepatient is suffering from a side-effect without regard as to whether thepatient is likely to be susceptible to the treatment services (e.g., asdetermined through a measurable metric).

Various embodiments of the present invention can be directed to computerprocessing systems, computer-implemented methods, apparatus and/orcomputer program products that facilitate the efficient, effective, andautonomous (e.g., without direct human guidance) determining an entity'sexpected susceptibility to one or more treatment services usingartificial intelligence (“AI”) technology. For instance, one or moreembodiments described herein can exploit a relationship between anentity's trust and an effectiveness of a treatment service regarding theentity, thereby providing a window into the entity's likelyresponsiveness to the one or more treatment services. Therefore, one ormore embodiments can use a relationship between a treatment service'seffectiveness and trust dynamics of the entity (e.g., the patient) toautonomously determine the entity's expected susceptibility dispositiontowards the treatment service and/or distribute the treatment servicebased on the expected susceptibility disposition.

For example, in one or more embodiments described herein can regardmeasuring, via one or more AI technologies, a trust dynamic associatedwith an entity (e.g., a patient) and/or can determine the entity'sexpected susceptibility disposition values towards one or more treatmentservices based on the measured trust dynamic. Measuring the trustdynamic can be achieved by analyzing one or more electroniccommunications, wherein commitments regarding the entity can beidentified and found to be fulfilled and/or unfulfilled. The trustdynamic can be indicative of the entity's responsiveness to a medicineand/or a treatment service. Further, various embodiments canautonomously distribute one or more treatment services based on one ormore expected susceptibility disposition values regarding the one ormore treatment services.

The computer processing systems, computer-implemented methods, apparatusand/or computer program products employ hardware and/or software tosolve problems that are highly technical in nature (e.g., determining anentity's expected susceptibility towards one or more treatment servicesbased on one or more analytically computed trust disposition values thatcan characterize one or more trust dynamics associated with an entity),that are not abstract and cannot be performed as a set of mental acts bya human. For example, an individual, or even a plurality of individuals,cannot readily collect, maintain, and/or analyze vast volumes of data asexpeditiously and/or efficiently as the various embodiments describedherein. Additionally, one or more embodiments described herein canutilize AI technologies that are autonomous in their nature tofacilitate determinations and/or predictions that cannot be readilyperformed by a human.

Within a patient's brain, correlations can be found between variousailments affecting the patient and one or more trust dynamics. Forexample, a correlation can exist between chronic pain and a patient'sdisposition to trust (e.g., in a medical professional and/or in amedicine). As used herein, the term “pain” can refer to an unpleasantsensory and/or emotional experience associated with actual or potentialtissue damage. Also, as used herein, the term “chronic pain” can referto pain that persists past a healing period, having widespread effectsthat can influence one or more levels of a nervous system. Chronic paincan persist for greater than or equal to three months and/or cansignificantly impact a person's psychological well-being.

In 2010, the American Academy of Pain Medicine (“AAPM”) estimated thatover 100 million Americans suffer from a chronic pain condition, andsaid conditions can cos the United States over 500 billion dollarsannually from health care costs and/or lost productivity. Diagnosingand/or treating chronic pain conditions can be difficult clinical tasksand/or can be further complicated by patient-physician dynamics, such astrust preservation over the course of the client-practitionerrelationship.

Chronic pain and trust, while being distinct experiences, can haveconsiderable overlap physiologically and/or psychologically. Forexample, from a neuroscience perspective, both phenomena can rely onsome of the same brain regions and/or similar brain networks involved inemotional processing, emotional regulation, interoceptive awareness,memory consolidation, memory recall, decision making, and/or socialattribution learning. The subject brain portions can include, forexample: the insula, the anterior cingulate cortex (“ACC”), theposterior cingulate cortex, the hippocampus, the amygdala, and/or thefrontal cortices (e.g., the medial prefrontal cortex (“mPFC”)). Evidenceof the shared neurocircuitry between aliments (e.g., chronic pain) andtrust can be found both at a structural level (e.g., by analyzing greyand/or white mater properties within a patient's brain) and/or at aneurophysiological level (e.g., by analyzing functional connects betweenbrain regions while a patient is at rest and/or performing a task). Dueat least to the described correlations, a patient's level of trust caninteract and/or influence their ailment (e.g., chronic pain), includingresponsiveness to a treatment regarding the ailment.

Furthermore, ailments, such as pain, and trust can have aspects oftrait-like and/or state-like qualities, which can be considered whenanalyzing trust dynamics of a patient. Regarding trait-like qualities, apatient's brain structure (e.g., grey matter density, volume, the shapeof subcortical regions, and/or the number of white matter connections)can facilitate predicting the patient's likelihood of developing anailment (e.g., chronic pain). Also, functional connectivity betweennucleus accumbens and/or the mPFC can facilitate predicting one or moretransitions between states of the ailment (e.g., a transition from acutepain to chronic pain). Thus, a patient's brain structure can predisposethe patient to a have an increased likelihood of suffering from theailment (e.g., chronic pain), even before a triggering event (e.g., aninjury).

Similarly, a patient's brain structure can also facilitate in predictingindividual differences in preconscious evaluation of another'strustworthiness (e.g., gray matter volume of the mPFC can be correlatedto a perceived untrustingness of others). For example, diffusion-tensorimaging (“DTI”) can show differences in functional anisotropy (“FA”)measures of white matter tract integrity around brain regions involvedin social cognition between individuals with normal vs impairedperceptions of trustworthiness. Since structural properties of graymatter and white matter do not change drastically in short time frames,such imaging suggest that individuals may be predisposed in the extentto which they can trust a person and/or situation.

Regarding state-like qualities, both trust and one or more ailments(e.g., chronic pain) can be non-stationary over time (e.g., due to apatient's life experiences in combination with their underlyingneuropsychological pre-dispositions). Also, the strength of functionalconnectivity between the dorsomedial prefrontal cortex (“dmPFC”) and theACC and/or between the insula and the hippocampus can change as afunction of perceived trustworthiness. Further, empathy towardsanother's ailment (e.g., pain) can be shaped by perceivedtrustworthiness and reflected in modulated neural processing withreduced activations of emotional processing regions when observingsomeone less trustworthy. Trust can also be affected by the patient'scurrent emotional state and/or mood. Similarly, some aliments (e.g.,pain) can have dynamic properties such as: varying durations, varyingintensities, and/or varying perceived locations. For example, longlasting ailments (e.g., chronic pain) can fluctuate about a meanintensity and/or exhibit peaks in intensity due to external triggers.For instance, neuroimaging can show that dynamics in subjective painratings can be reflected in the functional connectivity of sensoryand/or emotional brain networks. Also, neurophysiological activity canchange as a function of the intensity of pain and/or effectiveness of amedication, even a placebo. Further, like trust, a patient's mood canimpact the severity of an ailment (e.g., severity of pain) perceived bya patient. Thus, in addition to predisposing factors, one or moreailments (e.g., pain) and trust can both produce dynamics that can betracked in time and linked to internal and/or external perturbations.Moreover, one or more ailments (e.g., chronic pain) and trust can sharetemporal, affective, and/or social contexts. For example, both phenomenacan increase with age (e.g., older individuals can be more likely tosuffer from chronic pain and can be more trusting).

Thus, trust can play a significant role in any successful therapeuticinteraction and/or relationship. For example, in the case of chronicpain management, the ability to trust can influence the effectiveness ofpain relief medications. Factors potentially involved in trust dynamicsbetween a patient and a physician can span previous experiences, currentcontexts, and/or expected future occurrences. Example factors that caninfluence a physician's trust for a patient can include, but are notlimited to: the patient's subjective report and/or overall demeanor, thepatient's potential motives to seek treatment, the patient's potentiallevel of responsibility, previous experience with patients sufferingfrom the same ailment, discussions with colleagues who may or may nothave referred the patient, and/or medical culture at the time. Examplefactors that can influence a patient's trust for a physician caninclude, but are not limited to: the physician's interpersonalinteraction with the patient, the physician's reputation, whether thephysician's prescribed advice and/or therapy makes the patient feelbetter, previous experience with other physicians, previous experiencewith similar treatments for the ailment, previous experience withdifferent treatment for the ailment.

The claims and scope of the subject application, and any continuation,divisional or continuation-in-part applications claiming priority to thesubject application, exclude embodiments (e.g., systems, apparatus,methodologies, computer program products and computer readable storagemedia) directed to implanted electrical stimulation for pain treatmentand/or management.

FIG. 1 illustrates a block diagram of an example, non-limiting system100 that can determine one or more entities' susceptibility towards oneor more treatment services based on a determined trust disposition valuein accordance with one or more embodiments described herein. Aspects ofsystems (e.g., system 100 and the like), apparatuses or processes invarious embodiments of the present invention can constitute one or moremachine-executable components embodied within one or more machines,e.g., embodied in one or more computer readable mediums (or media)associated with one or more machines. Such components, when executed bythe one or more machines, e.g., computers, computing devices, virtualmachines, etc. can cause the machines to perform the operationsdescribed.

As shown in FIG. 1, the system 100 can comprise one or more servers 102,one or more networks 104, and/or one or more input devices 106. Theserver 102 can comprise control component 108. The control component 108can further comprise reception component 110, data analysis component112, and/or evaluation component 114. Also, the server 102 can compriseor otherwise be associated with at least one memory 116. The server 102can further comprise a system bus 118 that can couple to variouscomponents such as, but not limited to, the control component 108 andassociated components, memory 116 and/or a processor 120. While a server102 is illustrated in FIG. 1, in other embodiments, multiple devices ofvarious types can be associated with or comprise the features shown inFIG. 1. Further, the server 102 can communicate with a cloud computingenvironment via the one or more networks 104.

The one or more networks 104 can comprise wired and wireless networks,including, but not limited to, a cellular network, a wide area network(WAN) (e.g., the Internet) or a local area network (LAN). For example,the server 102 can communicate with the one or more input devices 106(and vice versa) using virtually any desired wired or wirelesstechnology including for example, but not limited to: cellular, WAN,wireless fidelity (Wi-Fi), Wi-Max, WLAN, Bluetooth technology, acombination thereof, and/or the like. Further, although in theembodiment shown the control component 108 can be provided on the one ormore servers 102, it should be appreciated that the architecture ofsystem 100 is not so limited. For example, the control component 108, orone or more components of control component 108, can be located atanother computer device, such as another server device, a client device,etc.

The one or more input devices 106 can comprise one or more computerizeddevices, which can include, but are not limited to: personal computers,desktop computers, laptop computers, cellular telephones (e.g., smartphones), computerized tablets (e.g., comprising a processor), smartwatches, keyboards, touch screens, mice, a combination thereof, and/orthe like. A user of the system 100 can utilize the one or more inputdevices 106 to input data into the system 100, thereby sharing (e.g.,via a direct connection and/or via the one or more networks 104) saiddata with the server 102. For example, the one or more input devices 106can send data to the reception component 110 (e.g., via a directconnection and/or via the one or more networks 104). Additionally, theone or more input devices 106 can comprise one or more displays that canpresent one or more outputs generated by the system 100 to a user. Forexample, the one or more displays can include, but are not limited to:cathode tube display (“CRT”), light-emitting diode display (“LED”),electroluminescent display (“ELD”), plasma display panel (“PDP”), liquidcrystal display (“LCD”), organic light-emitting diode display (“OLED”),a combination thereof, and/or the like.

In one or more embodiments, the control component 108 can analyze data(e.g., including data entered into the system 100 via the one or moreinput devices 106) to determine a trust disposition value using one ormore machine learning technologies. As used herein, the term “machinelearning technologies” can refer to an application of AI technologies toautomatically learn and/or improve from an experience (e.g., trainingdata) without explicit programming of the lesson learned and/orimproved. Also, as used herein, the term “trust disposition value” canrefer to a numerical value that can represent an entity's currentlikeliness to trust another individual, medicine, medical treatment,therapy, and/or situation.

Typical approaches in defining and/or estimating trust can be classifiedas conceptual approaches and/or computational approaches. Conceptualapproaches can consider the intuitive aspects of trust. For example, aperson providing the trust (“a trustor”) can be vulnerable to decisionsmade by a person receiving said trust (“a trustee”). However, conceptualapproaches lack a means to compute a trust disposition value that canrepresent said intuitive aspects of trust. Conventional computationalapproaches can compute a trust value, but are domain-specific andemphasize numerical heuristics, thereby ignoring the intuitivenessbehind trust. In one or more embodiments, the control component 108 canbridge the gap between the typical conceptual and computationalapproaches by determining a trust disposition value based on one or morecommitments. For example, the one or more commitments can be representedas “C(debtor, creditor, antecedent, consequent),” wherein the debtor cancommit to bring about the consequent for the creditor provided theantecedent holds. For instance “C(physician, patient, visiting thephysician, providing a medical report)” can be an exemplary commitment,wherein: the physician can be debtor, the patient can be the creditor,the antecedent can be visiting the physician, and/or the consequent canbe providing a medical report. When the physician provides the medicalreport, the example commitment is satisfied. If the physician fails toprovide the medical report, the example commitment is violated. Thus,one or more commitments can, for example, capture social relationshipsbetween a physician and a patient, thereby providing a basis forcomputing trust between the entities. Based at least on data regardingone or more commitments, the control component 108 can utilize machinelearning technologies to determine one or more trust disposition values.

The reception component 110 can receive the data entered by a user ofthe system 100 via the one or more input devices 106. The receptioncomponent 110 can be operatively coupled to the one or more inputdevices 106 directly (e.g., via an electrical connection) or indirectly(e.g., via the one or more networks 104). Additionally, the receptioncomponent 110 can be operatively coupled to one or more components ofthe server 102 (e.g., one or more component associated with the controlcomponent 108, system bus 118, processor 120, and/or memory 116)directly (e.g., via an electrical connection) or indirectly (e.g., viathe one or more networks 104).

In various embodiments, the reception component 110 can utilize one ormore AI technologies to identify and/or request data from an entity(e.g., a patient). For example, the reception component 110 can use oneor more chatbots (e.g., a talkbot, a chatterbot, a chatterbox, and/or anartificial conversational entity) to facilitate one or morecommunications between the entity and the reception component 110 (e.g.,via the one or more input devices 106 and/or the one or more networks104). The one or more communications can regard one or more commitmentsinvolving the subject entity (e.g., patient). As used herein, the term“chatbot” can refer to a computer program that can conduct one or moreconversations with an entity (e.g., a patient) via auditory and/ortextual methods. The one or more chatbots can be designed toconvincingly simulate how a human could converse, thereby passing theTuring test.

The one or more chatbots can use a sophisticated natural languageprocessing system (e.g., such as IBM WATSON®, other purveyors ofpre-build intents and/or dialogue flows, and/or like) and/or can scanfor keywords within an input (e.g., entered via the one or more inputdevices 106) and pull a stored reply comprising the most matchingkeywords and/or most similar wording patterning from a database. Forexample, the one or more chatbots can function based on one or morerules. In another example, the one or more chatbots can use AItechnology to understand language and/or continually learn fromconversations.

One or more entities can use the one or more input devices 106 tointeract with the reception component 110 (e.g., via the one or morenetworks 104) and provide data to the control component 108. Thereceived data can regard one or more communications regarding a subjectentity (e.g., a patient) and/or can be stored in one or more datasets122. The one or more datasets 122 can be located in the memory 116and/or in another location in a cloud computing environment (e.g.,accessible via the one or more networks 104). For example, the one ormore chatbots can inquire into one or more commitments involving theentity (e.g., the patient). For instance, the one or more chatbots cancommunicate regarding, for example: past experiences with a physician;past experiences with a subject medicine; past experiences with atreatment; expectations regarding a physician, medication, and/ortreatment; a level of satisfaction with a subject physician, medicationand/or treatment; and/or a perceived reputation of a subject physician,medication and/or treatment.

The data analysis component 112 can extract one or more features fromthe one or more datasets 122 and/or from one or more external sources(e.g., via the one or more networks 104). The extracted features canrepresent, for example, the debtor, the creditor, the antecedent, and/orthe consequent of a subject commitment. In other words, the dataanalysis component 112 can analyze the data received and/or stored bythe reception component 110 (e.g., via one or more chatbots) and extractone or more features that can represent one or more commitmentsinvolving the subject entity (e.g., subject patient).

Further, in one or more embodiments the data analysis component 112 cananalyze data (e.g., electronic communications) regarding the entity fromone or more external sources (e.g., data not entered via an engagementwith the reception component 110). For example, a subject entity (e.g.,a subject patient) can grant the data analysis component 112 access toone or more emails involving the entity. The data analysis component 112can analyze the one or more emails to identify one or more commitmentsand/or extract one or more features regarding the one or morecommitments.

The data analysis component 112 can use natural language processing toextract the one or more features, wherein the one or more features canbe ngrams (e.g., unigrams and/or bigrams), modal verbs, action verbs,and/or deadline from one or more sentences of the conversationsregarding the subject entity (e.g., via interaction with one or morechatbots and/or via communication with another individual, such as byemail). Subsequently, the data analysis component 112 can train aclassifier program (e.g., such as a support vector machine, a deepneural network, a random forest and/or decision forest, and/or like)based on the one or more extracted features.

In various embodiments, the evaluation component 114 can determine oneor more commitment operations based on the one or more extractedfeatures and/or generate one or more trust disposition values. Theevaluation component 114 can represent trust as binary evidence “<r,s>;”wherein “r” can represent a positive experience involving the entity andcan be greater than or equal to zero, and wherein “s” can represent anegative experience involving the entity and can be greater than orequal to zero. Thus, one or trust disposition values can be computed bythe evaluation component 114 as the probability of a positive outcome,which can be represented by “α” as shown in Equation 1 below.

$\begin{matrix}{\alpha = \frac{r}{r + s}} & {{Equation}\mspace{14mu} 1}\end{matrix}$

The control component 108 (e.g., via the reception component 110, thedata analysis component 112, and/or the evaluation component 114) canmaintain evidence “<r,s>” about a subject entity (e.g., a patient),wherein an initial evidence, “<r_(in), s_(in)>” can represent theentity's bias. One or more interactions between the entity and aphysician, medicine, and/or medical treatment (e.g., as characterized byone or more commitments represented via one or more communications) canyield a positive, negative, or neutral experience. Thus, with eachinteraction (e.g., with each communication captured by the one or morechatbots and/or an external source such as email) the entity's initialevidence can be updated by adding “<i_(r),0>”, “<0,i_(s)>” and “<λi_(r),(1−λ)i_(s)>”; wherein λ∈[0,1], wherein “i_(r)” can represent newevidence of a positive experience, and wherein “i_(s)” can represent newevidence of a negative experience. Therefore, the evaluation component114 can determine an entity's trust disposition value based on at leastthe five parameters: “i_(r)”, “i_(s)”, “r_(in)”, “s_(in)”, and/or “λ.”To learn the parameters based on positive experiences (e.g., representedby “E⁺”), negative experiences (e.g., represented by “E⁻”), and/orneutral experiences (e.g., represented by “E”), the evaluation component114 can represent the trust disposition value in accordance withEquation 2 below.

$\begin{matrix}{\alpha = \frac{r_{in} + \left( {i_{r} \star E^{+}} \right) + {\lambda \star i_{r} \star E}}{r_{in} + s_{in} + \left( {i_{r} \star E^{+}} \right) + \left( {i_{s} \star E^{-}} \right) + {E\left( {{\lambda \star i_{r}} + {\left( {1 - \lambda} \right)i_{s}}} \right)}}} & {{Equation}\mspace{14mu} 2}\end{matrix}$

For example, Table 1, provided below, can depict exemplary analysis(e.g., via the data analysis component 112) and/or evaluation (e.g., viaevaluation component 114) of multiple email interactions (e.g.,communications involving one or more commitments) involving an entity.As shown in Table 1, commitment operations can be determined based onone or more features extracted from the emails. The commitmentoperations can delineate, for example, that: a commitment has beencreated (e.g., represented by “create(C₁),” which can indicate that afirst commitment has been created, and/or by “create(C₂),” which canindicate that a second commitment has been created); that a commitmenthas been fulfilled (e.g., represented by “satisfied (C₁);” and/or that acommitment has been violated (e.g., represented by “violate(C₂).”

TABLE 1 Sender Receiver Email Operation Kim Dorothy I will check withAlliance Travel create(C₁) Agency . . . Kim Dorothy I checked with ourTravel satisfied(C₁) Agency . . . Rob Kim By Wednesday, please send allcopies create(C₂) of your documentation . . . Kim Rob Rob, pleaseforgive me for not violate(C₂) sending . . .

Additionally, Table 2, presented below, can depict computed trustdisposition values that can represent relationships of trustcharacterized by the exemplary email interactions depicted in Table 1.As shown in Table 1, there are two exemplary email interactions betweenKim and Dorothy. As shown in Table 2, amongst the two said emailinteractions, Kim's trust in Dorothy remains neutral (e.g., representedby “E=2”) at least because Dorothy has neither fulfilled a commitment(e.g., represented by “E⁺=0”) nor violated a commitment (e.g.,represented by “E⁻=0”). Also, as shown in Table 2, amongst the two emailinteractions between Kim and Dorothy, Dorothy's trust in Kim is positiveat least because: the first email interaction created a commitment, andwas thus neutral (e.g., represented by “E=1”), and the second emailinteraction fulfilled a commitment (e.g., represented by “E⁺=1”) withoutviolating a commitment between the entities (e.g., represented by“E⁻=0”). Further, as shown in Table 2: “S1-S5” can represent possibletrust disposition values created by analyzing the communications and/orinteractions between Kim and Dorothy.

TABLE 2 Trust Pairs Experiences S1 S2 S3 S4 S5 Kim → Dorothy E = 2, E⁺ =0, E⁻ = 0 0.45 0.6 0.8 0.6 0.56 Dorothy → Kim E = 1, E⁺ = 1, E⁻ = 0 0.70.9 0.8 0.76 0.84

One of ordinary skill in the art will recognize that while the exemplarydata depicted in Table 1 and/or Table 2 can be derived from an externalsource (e.g., one or more email accounts), data analyzed and/orevaluated by the control component 108 (e.g., via the data analysiscomponent 112 and/or the evaluation component 114) can be receivedthrough one or more communications with a subject entity by AItechnology (e.g., one or more chatbots utilized by the receptioncomponent 110). Furthermore, while the exemplary data depicted in Table1 and/or Table 2 depicts one or more trust relations (e.g.,characterized by one or more trust disposition values) between twoindividuals (e.g., a patient/physician relationship), one or more trustdisposition values can also characterize trust relations between anentity and an object (e.g., a medicine) and/or an event (e.g., medicaltreatment and/or therapy). For example, data received by the receptioncomponent 110 (e.g., via one or more chatbots) and/or analyzed by thedata analysis component 112 can regard an entity's past experiencesand/or future expectations regarding a medicine and/or medicaltreatment. For instance, one or more commitments comprised with the oneor more communications can regard fulfillment and/or violation ofresults expected to be achieved by a medicine and/or medical treatment.

Thus, in various embodiments the control component 108 (e.g., via thereception component 110) can receive data representing one or morecommunications involving an entity (e.g., a patient). The received datacan regard one or more commitments that can influence the entitiesdisposition to provide trust. The control component 108 can determineone or more initial trust values (e.g., an initial trust dispositionvalue) based on the data. Also, the control component 108 can extractone or more features from the data to train machine learningtechnologies, which can update the initial trust value to representtrust predictions that can characterize future trust relations involvingthe entity (e.g., updated trust disposition values). Thus, the controlcomponent 108 can determine a trust disposition value regarding one ormore relationships involving an entity (e.g., a patient) and/or canupdate the trust disposition value as new data (e.g., communicationsinvolving the entity) is received (e.g., via the reception component 110and/or an external source such as email correspondences). Furthermore,in one or more embodiments the control component 108 can maintain thecurrency of one or more of the trust disposition values by updating saidtrust disposition values based on the periodic collection of new data.For example, the reception component 110 can receive new data (e.g., viathe one or more chatbots) and/or the data analysis component 112 cananalyze new data at predefined periodic intervals (e.g., each day, eachweek, each month, and/or the like). Further the evaluation component 114can update one or more trust disposition values based on the most recentdata and/or data analysis (e.g., most recent extract features). In otherwords, the functions of the control component 108 can be repeatedperiodically to ensure the trust disposition values are up-to-date andaccurately represent the current disposition of an entity.

FIG. 2 illustrates a block diagram of the example, non-limiting system100 further comprising model component 202 in accordance with one ormore embodiments described herein. Repetitive description of likeelements employed in other embodiments described herein is omitted forsake of brevity.

The model component 202 can generate one or more trust graphs, which canfacilitate determining trust disposition values regarding a newrelationship involving the entity based on one or more trust dispositionvalues regarding other relationships involving the entity. As usedherein, the term “trust graph” can refer to a computer model thatdepicts linkages between individuals and/or objects (e.g., medicinesand/or medical treatments) through which trust can be represented (e.g.,via one or more trust disposition values) and/or traversedalgorithmically. For example, one or more linkages of a trust graph candepict how a subject entity (e.g., a patient) can be indirectlycorrelated to a target individual and/or object through one or moreintermediate individuals and/or objects. Further, trust dispositionvalues associated with the subject entity and the one or moreintermediate individuals and/or object can facilitate predicting a trustdisposition value associated with an indirect relationship between thesubject entity and the target individual and/or object, despite a lackof previous experience and/or communication between the subject entityand/or the target individual and/or object.

For instance, one or more trust graphs generated by the model component202 can depict a direct linkage between Fred and Fred's friend Harry.Further, the one or more trust graphs can depict a high trustdisposition value associated with the direct link between Fred andHarry, thereby indicating, for example, that Fred has a high dispositionof trust towards Harry. Also, the one or more trust graphs can depict adirect linkage between Harry and Harry's doctor, Dr. Lou. Further, theone or more trust graphs can depict a high trust disposition valueassociated with the direct link between Harry and Dr. Lou, therebyindicating, for example, that Harry has a high disposition of trusttowards Dr. Lou. Harry can refer Fred to Dr. Lou, wherein a trustdisposition value regarding Fred's disposition of trust towards Dr. Loucan be predicted at least because Harry can act as an intermediary toestablish an indirect linkage between Fred and Dr. Lou. Thus, the modelcomponent 202, via the one or more trust graphs, can predict that Fredhas a high disposition of trust towards Dr. Lou despite no previousencounters between the two individuals based on the high dispositionvalues associated with the direct linkages (e.g., the direct linkbetween Fred and Harry and/or the direct link between Harry and Dr. Lou)that form the indirect link between Fred and Dr. Lou.

Thus, in various embodiments the model component 202 can predict one ormore disposition values associated with one or more new relationshipsbased on one or more disposition values associated with establishedrelationships that have influenced the one or more new relationships.Wherein a subject relationship (e.g., between a patient and a physician,between a patient and a medicine, and/or between a patient and a medicaltreatment) is established through one or more intermediaryrelationships, a trust disposition value characterizing the subjectrelationship can be predicted (e.g., via the model component 202) basedon one or more trust disposition values associated with the one or moreintermediary relationships. In other words, the model component 202 canpredict one or more trust disposition values associated with an indirectrelationship (e.g., a relationship that can lack previous interactionbetween subject entities) based on one or more trust disposition valuesassociated with one or more direct relationships (e.g., a relationshipthat can be characterized by past interactions, such as commitments,between the subject entities).

FIG. 3 illustrates a block diagram of the example, non-limiting system100 further comprising assessment component 302 in accordance with oneor more embodiments described herein. Repetitive description of likeelements employed in other embodiments described herein is omitted forsake of brevity.

In various embodiments, the assessment component 302 can determine oneor more susceptibility disposition values regarding an entity's expectedsusceptibility towards one or more treatment services based on one ormore determined and/or predicted trust disposition values. As usedherein, the term “treatment service” can refer to one or more servicesdesigned to treat and/or inhibit side-effects and/or adverse healthconditions (e.g., brain disease) afflicting an entity (e.g., a patient)as a result of consumption of a chemical compound (e.g., a medicine). Anexample side-effect and/or adverse health condition can be a chemicaldependency induced by a chemical compound (e.g., a medicine), wherein apatient consuming the medicine is encouraged, via the experience of achemical reaction, to consume more of the chemical compound (e.g., viaunrecommended methods, dosages, and/or frequency). Treatment servicescan engage an entity via visual, textural, and/or audible means. Forexample, treatment services can be presented via a video, a pamphlet, abook, a recording, a message (e.g., an email service and/or a textservice), a counseling session (e.g., with a therapist and/or aphycologist), a webinar, a phone call, a combination thereof, and/or thelike. Additionally, example treatment services can include, but are notlimited to, enhanced monitoring of side-effects and/or adverse healthconditions afflicting an entity. Further, one or more treatment services(e.g., counseling sessions) can be facilitated by real and/or virtualhosts (e.g., virtual counselors). Moreover, treatment services can beoffered to the entity and/or a close relation to the entity (e.g., afriend, family member, spouse, co-worker, and/or the like).

In one or more embodiments, the control component 108 (e.g., via thereception component 110, the data analysis component 112, and/or theevaluation component 114) can determine trust disposition valuesregarding one or more relationships associated with an entity. Forexample, an entity's trust in one or more medical professionals can becharacterized by a first trust disposition value. The entity's trust ina chemical compound (e.g., a medicine) can be characterized by a secondtrust disposition value. The entity's trust in a side affect and/oradverse health condition associated with a chemical compound, such asmedicine, (e.g., trust in the likelihood in said side-effect and/oadverse health condition occurring) can be characterized by a thirdtrust disposition. Further, an entity's trust disposition towardsindividuals and/or events in general can be characterized by a fourthtrust disposition value (e.g., an and/or cumulative average trustdisposition value). Thus, one of ordinary skill in the art willrecognize that an entity can be characterized by a plurality of trustdisposition values and/or predicted trust disposition values (e.g., viaone or more trust graphs) that can characterize various relationshipsbetween the entity and an individual and/or an object (e.g., a chemicalcompound, a side-effect, and/or a treatment service).

As discussed herein, an entity's (e.g., a patient) trust in anindividual (e.g., a physician) and/or an object (e.g., a medicine and/ora medical treatment) can affect one or more responses the entity hasregarding a service of the individual and/or object. For example, apatient's trust in his/her physician can influence the effectiveness ofone or more treatment services prescribed, endorsed, and/or administeredby the physician with regards to the patient. In another example, apatient's expected trust in one or more treatment services can influencethe effectiveness of the one or more treatment services. Thus, the oneor more trust disposition values determined by the control component 108(e.g., via the data analysis component 112, the evaluation component114, and/or the model component 202) can be indicative of an expectedeffectiveness of one or more medicines and/or treatment services.

For example, wherein an entity has a large amount of trust in atreatment service (e.g., due to one or more positive past experienceswith the treatment service and/or one or more positive indirectrelationships, such as recommendations, with the treatment service), thetreatment service can demonstrate a high effectiveness in treatingand/or inhibiting one or more side-effects and/or adverse healthconditions resulting from a chemical compound (e.g., chemicaldependency). In another example, wherein the entity has a low amount oftrust in a treatment service (e.g., due to one or more negative pastexperiences with the treatment service and/or one or more negativeindirect relationships, such as recommendations, with the treatmentservice), the treatment service can demonstrate a low effectiveness intreating and/or inhibiting one or more side-effects and/or adversehealth conditions resulting from a chemical compound (e.g., chemicaldependency).

In one or more embodiments, the assessment component 302 can compareand/or analyze one or more trust disposition values to determine one ormore susceptibility disposition values. As used herein, the term“susceptibility disposition value” can refer to an entity's expectedsusceptibility towards one or more treatment services being effective ininhibiting and/or treating a side-effect and/or adverse health conditioncaused by a chemical compound (e.g., a medicine). For example, thesusceptibility disposition values can be based on various factorsincluding, but not limited to: a likelihood that the entity is sufferingfrom a side-effect and/or adverse health condition caused by a chemicalcompound (e.g., a medicine); a likelihood an entity will be responsiveto one or more treatment services; the entity's expected dispositiontowards the side-effect and/or adverse health condition; a combinationthereof, and/or the like. Each factor can be characterized by one ormore trust disposition values, individually and/or collectively.Additionally, one or more medical diagnoses (e.g., performed by amedical professional) can supplement characterization of the factors,alone or in conjunction with one or more trust disposition values.

For example, one or more trust disposition values can be indicative ofone or more risk factors related to vulnerability of the entity to asubject side-effect and/or adverse health condition (e.g., chemicaldependency). In another example, one or more trust disposition valuescan characterize relationships that can affect an expectedresponsiveness to one or more treatment services; including, forexample, an entity's disposition of trust towards: a medicalprofessional who recommends the one or more treatment services, one ormore procedures and/or features of the one or more treatment services,one or more entities that have past experience with the one or moretreatment services, a combination thereof, and/or the like. In a furtherexample, one or more trust disposition values can characterize: theentity's past positive or negative experiences with side-effects and/oradverse health conditions resulting from the chemical compound (e.g., amedicine); and/or future positive and/or negative experiences that theentity expects regarding side-effects and/or adverse health conditionsresulting from the chemical compound (e.g., a medicine).

Thus, the assessment component 302 can determine the one or moresusceptibility values based on one or more factors associated withside-effects and/or adverse health conditions resulting from a chemicalcompound (e.g., a medicine), which can be characterized by one or moretrust disposition values. For example, the assessment component 302 candetermine a low susceptibility disposition value (e.g., indicative thatan entity can be less susceptible) towards one or more treatmentservices based on, for example, one or more trust disposition valuescharacterizing that: an entity can be less likely (e.g., as compared topredefined threshold) to be suffering from side-effects and/or adversehealth conditions resulting from a chemical compound (e.g., a medicine);an entity can be less likely (e.g., as compared to predefined threshold)to be responsive to treatment services (e.g., due to a lack of trust inthe treatment services); an entity trusts that the side-effects and/oradverse health conditions resulting from a chemical compound (e.g., amedicine) have resulted in and/or will result in positive experiences; acombination thereof, and/or the like. In another example, the assessmentcomponent 302 can determine a high susceptibility disposition value(e.g., indicative that an entity can be likely susceptible) towards oneor more treatment services based on, for example, one or more trustdisposition values characterizing that: an entity can be likely (e.g.,as compared to predefined threshold) to be suffering from side-effectsand/or adverse health conditions resulting from a chemical compound(e.g., a medicine); an entity can be likely (e.g., as compared topredefined threshold) to be responsive to treatment services (e.g., dueto the existence of trust in the treatment services); an entity truststhat the side-effects and/or adverse health conditions resulting from achemical compound (e.g., a medicine) have resulted in and/or will resultin negative experiences; a combination thereof, and/or the like.

The assessment component 302 can assess one or more trust dispositionvalues by comparing the trust disposition values to one or morepredefined thresholds to determine their likeliness. Additionally, theassessment component 302 can assess one or more trust disposition valuesbased on (e.g., in response to) assessment of one or more other trustdisposition values. For example, the assessment of one or more trustdispositions (e.g., characterizing one or more relationships associatedwith one or more factors of the susceptibility disposition value) ascompared to a predefined threshold can serve as a criterion for theassessment of one or more other trust disposition values (e.g.,characterizing one or more other relationships associated with one ormore other factors of the susceptibility disposition value).

In one or more embodiments, the assessment component 302 can determinethe susceptibility disposition value based on a single factor (e.g.,likely responsiveness to treatment services, or a likeliness to besuffering from a side-effect and/or adverse health condition), andthereby the one or more trust disposition values that can characterizethe single factor. In one or more other embodiments, the assessmentcomponent 302 can determine the susceptibility disposition value basedon multiple factors (e.g., likeliness of experiencing a side-effectand/or adverse health condition from a chemical compound, likelyresponsiveness to treatment services, and/or expected dispositiontowards the side-effect and/or adverse health condition), and therebythe one or more trust disposition values that can characterize themultiple factors. For example, the susceptibility disposition value canbe based on: whether one or more first trust disposition values, whichfor example can characterize a likelihood that the subject entity isexperiencing a side-effect and/or adverse health condition from achemical compound, meet a defined criterion (e.g., as compared to apredefined threshold); and/or a difference between one or more secondtrust disposition values, which for example can characterize the subjectentity's likely responsiveness to one or more treatment services, andone or more third trust disposition values, which for example cancharacterize the subject entity's disposition towards the side-effectsand/or adverse health conditions caused by the chemical compound (e.g.,the medicine).

FIG. 4 illustrates a block diagram of the example, non-limiting system100 further comprising distribution component 402 in accordance with oneor more embodiments described herein. Repetitive description of likeelements employed in other embodiments described herein is omitted forsake of brevity. The distribution component 402 can facilitatedistribution of one or more treatment services to one or more entitiesbased on the one or more susceptibility disposition values. Thedistribution component 402 can be: located within the server 102,located outside the server 102, and/or a combination thereof (e.g., viaimplementation of a cloud computing environment).

In one or more embodiments, the assessment component 302 can storedetermined susceptibility disposition values in one or moresusceptibility databases, which can be located in the memory 116 and/ora cloud computing environment. Further, the assessment component 302 canupdate the one or more susceptibility databases as one or moresusceptibility disposition values change based on updated data (e.g.,updated trust disposition values computed based on newly received and/orcollected data). The distribution component 402 can monitor the one ormore susceptibility databases and identify when one or moresusceptibility disposition values are greater than or equal to apredefined threshold. In response to identifying a susceptibilitydisposition value greater than or equal to the predefined threshold, thedistribution component 402 can distribute one or more treatment servicesto one or more entities associated with the subject susceptibilitydisposition value. For example, the distribution component 402 candistribute one or more treatment services via the one or more networks104. Further, the distribution component 402 can distribute the one ormore treatment services to the one or more input devices 106 tofacilitate presentation to the one or more entities. In addition, and/oralternatively, the distribution component 402 can generate one or morerecommendations regarding whether to distribute the one or moretreatment services to the subject one or more entities based on the oneor more susceptibility disposition values. For example, the one or moregenerated recommendations can: encourage distribution of the one or moretreatment services to the one or more entities based on the one or moresusceptibility disposition values being greater than or equal to apredefined threshold; or discourage distribution of the one or moretreatment services to the one or more entities based on the one or moresusceptibility disposition values being lower than a predefinedthreshold. The generated recommendation (e.g., a message conveyed viatext, audio, and/or video) can be sent (e.g., via the one or morenetworks 104) by the distribution component 402 to one or more medicalprofessionals and/or the one or more entities.

In various embodiments, the assessment component 302 can send a commandcode (e.g., via the one or more networks 104) to the distributioncomponent 402, thereby instructing the distribution component 402 tofacilitate distribution of the one or more treatment services based onthe computed susceptibility disposition values. For example, theassessment component 302 can send the command code in response todetermining a susceptibility disposition value that is greater than orequal to a predefined threshold. In response to receiving the commandcode, the distribution component 402 can distribute one or moretreatment services to one or more entities associated with the subjectsusceptibility disposition value. For example, the distributioncomponent 402 can distribute one or more treatment services via the oneor more networks 104. Further, the distribution component 402 candistribute the one or more treatment services to the one or more inputdevices 106 to facilitate presentation to the one or more entities. Inaddition, and/or alternatively, the command code can instruct thedistribution component 402 to generate one or more recommendationsregarding whether to distribute the one or more treatment services tothe subject one or more entities based on the one or more susceptibilitydisposition values. For example, the one or more generatedrecommendations can: encourage distribution of the one or more treatmentservices to the one or more entities based on the one or moresusceptibility disposition values being greater than or equal to apredefined threshold; or discourage distribution of the one or moretreatment services to the one or more entities based on the one or moresusceptibility disposition values being lower than a predefinedthreshold. The generated recommendation (e.g., a message conveyed viatext, audio, and/or video) can be sent (e.g., via the one or morenetworks 104) by the distribution component 402 to one or more medicalprofessionals and/or the one or more entities.

FIG. 5 illustrates a block diagram of the example, non-limiting system100 further comprising one or more medical centers 502 in accordancewith one or more embodiments described herein. Repetitive description oflike elements employed in other embodiments described herein is omittedfor sake of brevity.

As shown in FIG. 5, the server 102 can be operatively coupled (e.g.,directly and/or indirectly via the one or more networks 104) to one ormore medical centers 502. The one or medical centers 502 can befacilities where medical assistance, medical diagnoses, medical reports,medical therapies, and/or medical treatments can be provided. Examplemedical centers 502 can include, but are not limited to: hospitals,urgent care clinics, physician practices, nursing homes, a combinationthereof, and/or the like. In one or more embodiments, the server 102(e.g., the control component 108) can receive medical data from the oneor more medical centers 502, wherein the medical data can regard asubject entity and can be used by the control component 108 to determineone or more trust disposition values. For example, one or more medicalprofessionals (e.g., physicians) at the one or more medical centers 502can capture images of a subject individual's brain structure to assessone or more neurological features. As previously described herein, anindividual's brain structure can be indicative of the individual'spredisposition to an ailment (e.g., pain and/or chronic pain) and/orwillingness to trust. The medical data (e.g., one or more brain imagesand/or medical history) can be sent to the server 102 (e.g., the controlcomponent 108) and used (e.g., via the evaluation component 114) tocreate a predisposition baseline, which can be further augmented basedon the other various forms of data described herein.

In various embodiments, the system 100 (e.g., via the control component108) can autonomously: collect data regarding trust dynamics of anentity (e.g., data regarding communications that indicate thefulfillment or violation of one or more commitments); determine one ortrust disposition values based on the collected data; maintain thecurrency of the one or more trust disposition values using, for example,one or more machine learning technologies; generate one or more trustgraphs to predict one or more trust disposition values that cancharacterize indirect relationships involving the subject entity;determine one or more susceptibility disposition values based on the oneor more trust disposition values; and/or distribute one or moretreatment services based on the one or more susceptibility dispositionvalues. Therefore, the system 100 tailor one or more treatment servicesassociated to an entity based on recent experience involving the entitythat can affect the entity's susceptibility towards the one or moretreatment services. Further, the system 100 can determine an entity'ssusceptibility towards one or more treatment services expeditiously bynegating intervention by a medical professional (e.g., a physician).Thus, an individual subject to the analyses and/or evaluations of thesystem 100 can benefit from up-to-date treatment services distributedduring periods of optimal expected effectiveness based on theindividual's current disposition of trust.

FIG. 6 illustrates a flow diagram of an example, non-limiting method 600that can facilitate determining one or more susceptibility dispositionvalues that can characterize one or more entities' susceptibilitytowards one or more treatment services based on one or more computedtrust disposition values in accordance with one or more embodimentsdescribed herein. Repetitive description of like elements employed inother embodiments described herein is omitted for sake of brevity.

At 602, the method 600 can comprise generating, by a system 100 (e.g.,via reception component 110 and/or data analysis component 112)operatively coupled to a processor 120, one or more datasets (e.g.,dataset 122) that can represent a plurality of features extracted fromone or more communications regarding an entity (e.g., a patient). In oneor more embodiments, the one or more communications can be initiatedand/or facilitated by one or more AI systems (e.g., one or morechatbots). Additionally, the one or more communications can be accessedvia an external source, such as an email account. Further, the one ormore communications can regard one or more commitments involving theentity, and/or the one or more extracted features can delineate whetherthe one or more commitments were fulfilled or violated. Thus, in variousembodiments the one or more communications can be indicative of theentity's past positive and/or negative experiences with an individual(e.g., a physician) and/or object (e.g., a medicine and/or medicaltreatment).

At 604, the method 600 can comprise determining, by the system 100(e.g., via the data analysis component 112 and/or the evaluationcomponent 114), one or more trust disposition values associated with theentity by analyzing the one or more datasets using machine learningtechnology. For example, the determining can comprise computing alikelihood that the entity will trust an individual (e.g., a physician)and/or object (e.g., a medicine and/or medical treatment) based on pastpositive and/or negative experiences. For instance, the system 100(e.g., via the evaluation component 114) can utilize Equation 1 and/orEquation 2 to compute the one or more trust disposition values.

At 606, the method 600 can comprise determining, by the system 100(e.g., via the assessment component 302), one or more susceptibilitydisposition values that can measure a susceptibility of one or moreentities to one or more treatment services based on the one or moretrust disposition values. For example, the determining at 606 cancompare one or more trust disposition values to a predefined thresholdand/or to other trust disposition values (e.g., which can also bedetermined at 604). Further, the determining can be based on one or morefactors regarding the one or more entities' susceptibility towards theone or more treatment services, wherein the one or more factors can becharacterized by the one or more trust disposition values. Examplefactors regarding the one or more entities' susceptibility towards theone or more treatment services can include, but are not limited to: alikelihood that the one or more entities are suffering from one or moreside-effects and/or adverse health conditions caused by one or morechemical compounds (e.g., medicines); a likelihood the one or moreentities will be responsive to one or more treatment services; the oneor more entities' expected disposition towards the one or moreside-effect and/or adverse health condition; a combination thereof,and/or the like.

FIG. 7 illustrates a flow diagram of an example, non-limiting method 700that can facilitate determining one or more susceptibility dispositionvalues that can characterize one or more entities' susceptibilitytowards one or more treatment services based on one or more computedtrust disposition values in accordance with one or more embodimentsdescribed herein. Repetitive description of like elements employed inother embodiments described herein is omitted for sake of brevity.

At 702, the method 700 can comprise generating, by a system 100 (e.g.,via reception component 110 and/or data analysis component 112)operatively coupled to a processor 120, one or more datasets (e.g.,dataset 122) that can represent a plurality of features extracted fromone or more communications regarding an entity (e.g., a patient). In oneor more embodiments, the one or more communications can be initiatedand/or facilitated by one or more AI systems (e.g., one or morechatbots). Additionally, the one or more communications can be accessedvia an external source, such as an email account. Further, the one ormore communications can regard one or more commitments involving theentity, and/or the one or more extracted features can delineate whetherthe one or more commitments were fulfilled or violated. Thus, in variousembodiments the one or more communications can be indicative of theentity's past positive and/or negative experiences with an individual(e.g., a physician) and/or object (e.g., a medicine and/or medicaltreatment).

At 704, the method 700 can comprise determining, by the system 100(e.g., via the data analysis component 112 and/or the evaluationcomponent 114), one or more trust disposition values associated with theentity by analyzing the one or more datasets using machine learningtechnology. For example, the determining can comprise computing alikelihood that the entity will trust an individual (e.g., a physician)and/or object (e.g., a medicine and/or medical treatment) based on pastpositive and/or negative experiences. For instance, the system 100(e.g., via the evaluation component 114) can utilize Equation 1 and/orEquation 2 to compute the one or more trust disposition values.

At 706, the method 700 can comprise maintaining, by the system 100(e.g., via the reception component 110, the data analysis component 112,and/or the evaluation component 114), a currency (e.g., a currentness)of the one or more trust disposition values via periodic communicationswith the entity using one or more AI technologies. For example, one ormore chatbots (e.g., utilized by the reception component 110) can engagethe entity in conversation in predefined intervals (e.g., each day, eachweek, each month, and/or the like). Further, the one or more trustdisposition values can be updated (e.g., via the evaluation component114) based on new data extracted (e.g., via the data analysis component112) from the most current conversation with the chatbot.

At 708, the method 700 can comprise generating, by the system 100 (e.g.,via the model component 202), one or more trust graphs that canrepresent a plurality of trust disposition values associated with aplurality of entities related to the entity. The plurality of entitiescan be in direct relationships with the entity or indirect relationshipswith the entity (e.g., relationships established through one or moreintermediate relationships). The one or more trust graphs can facilitatedetermining one or more trust disposition values regarding relationshipsthat lack historical data (e.g., lack communications between the subjectentities and/or previous commitments between the entities).

At 710, the method 700 can comprise determining, by the system 100(e.g., via the assessment component 302), one or more susceptibilitydisposition values that can measure a susceptibility of one or moreentities to one or more treatment services based on the one or moretrust disposition values. For example, the determining at 710 cancompare one or more trust disposition values to a predefined thresholdand/or to other trust disposition values (e.g., which can also bedetermined at 704). Further, the determining can be based on one or morefactors regarding the one or more entities' susceptibility towards theone or more treatment services, wherein the one or more factors can becharacterized by the one or more trust disposition values. Examplefactors regarding the one or more entities' susceptibility towards theone or more treatment services can include, but are not limited to: alikelihood that the one or more entities are suffering from one or moreside-effects and/or adverse health conditions caused by one or morechemical compounds (e.g., medicines); a likelihood the one or moreentities will be responsive to one or more treatment services; the oneor more entities' expected disposition towards the one or moreside-effect and/or adverse health condition; a combination thereof,and/or the like.

At 712, the method 700 can comprise generating, by the system 100 (e.g.,via the control component 108), one or more recommendations regardingwhether to distribute the one or more treatment services based on theone or more susceptibility disposition values. The one or more treatmentservices can regard, for example, one or more chemical effects affectingthe one or more entities as a result of consuming one or more chemicalcompounds (e.g., medicines). For example, the one or more generatedrecommendations can: encourage distribution of the one or more treatmentservices to the one or more entities based on the one or moresusceptibility disposition values being greater than or equal to apredefined threshold; or discourage distribution of the one or moretreatment services to the one or more entities based on the one or moresusceptibility disposition values being lower than a predefinedthreshold. Also, the distribution component 402 can send the one or moregenerated recommendations to one or more medical professionals via theone or more networks 104. Further, the distribution component 402 cansend the one or more generated recommendations to the one or moreentities via the one or more networks 104.

Additionally, or alternatively, at 712 the method 700 can comprisedistributing (e.g., autonomously), by the system 100 (e.g., via thedistribution component 402), the one or more treatment services based onthe one or more susceptibility disposition values. The one or moretreatment services can regard, for example, one or more chemical effectsaffecting the one or more entities as a result of consuming one or morechemical compounds (e.g., medicines). For example, the distributioncomponent 402 can send the one or more treatment services to the one ormore entities via the one or more networks 104. For instance, the one ormore entities can be presented the one or more treatment services viathe one or more input devices 106.

It is to be understood that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported, providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure that includes anetwork of interconnected nodes.

Referring now to FIG. 8, illustrative cloud computing environment 800 isdepicted. Repetitive description of like elements employed in otherembodiments described herein is omitted for sake of brevity. As shown,cloud computing environment 800 includes one or more cloud computingnodes 802 with which local computing devices used by cloud consumers,such as, for example, personal digital assistant (PDA) or cellulartelephone 804, desktop computer 806, laptop computer 808, and/orautomobile computer system 810 may communicate. Nodes 802 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 800 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 804-810shown in FIG. 8 are intended to be illustrative only and that computingnodes 802 and cloud computing environment 800 can communicate with anytype of computerized device over any type of network and/or networkaddressable connection (e.g., using a web browser).

Referring now to FIG. 9, a set of functional abstraction layers providedby cloud computing environment 800 (FIG. 8) is shown. Repetitivedescription of like elements employed in other embodiments describedherein is omitted for sake of brevity. It should be understood inadvance that the components, layers, and functions shown in FIG. 9 areintended to be illustrative only and embodiments of the invention arenot limited thereto. As depicted, the following layers and correspondingfunctions are provided.

Hardware and software layer 902 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 904;RISC (Reduced Instruction Set Computer) architecture based servers 906;servers 908; blade servers 910; storage devices 912; and networks andnetworking components 914. In some embodiments, software componentsinclude network application server software 916 and database software918.

Virtualization layer 920 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers922; virtual storage 924; virtual networks 926, including virtualprivate networks; virtual applications and operating systems 928; andvirtual clients 930.

In one example, management layer 932 may provide the functions describedbelow. Resource provisioning 934 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 936provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 938 provides access to the cloud computing environment forconsumers and system administrators. Service level management 940provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 942 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 944 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 946; software development and lifecycle management 948;virtual classroom education delivery 950; data analytics processing 952;transaction processing 954; and assessing an entity's susceptibility toa treatment service 956. Various embodiments of the present inventioncan utilize the cloud computing environment described with reference toFIGS. 8 and 9 to determine a trust disposition value associated with oneor more entities and/or determine the susceptibility of the one or moreentities to one or more treatment services based on the trustdisposition value.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

In order to provide a context for the various aspects of the disclosedsubject matter, FIG. 10 as well as the following discussion are intendedto provide a general description of a suitable environment in which thevarious aspects of the disclosed subject matter can be implemented. FIG.10 illustrates a block diagram of an example, non-limiting operatingenvironment in which one or more embodiments described herein can befacilitated. Repetitive description of like elements employed in otherembodiments described herein is omitted for sake of brevity. Withreference to FIG. 10, a suitable operating environment 1000 forimplementing various aspects of this disclosure can include a computer1012. The computer 1012 can also include a processing unit 1014, asystem memory 1016, and a system bus 1018. The system bus 1018 canoperably couple system components including, but not limited to, thesystem memory 1016 to the processing unit 1014. The processing unit 1014can be any of various available processors. Dual microprocessors andother multiprocessor architectures also can be employed as theprocessing unit 1014. The system bus 1018 can be any of several types ofbus structures including the memory bus or memory controller, aperipheral bus or external bus, and/or a local bus using any variety ofavailable bus architectures including, but not limited to, IndustrialStandard Architecture (ISA), Micro-Channel Architecture (MSA), ExtendedISA (EISA), Intelligent Drive Electronics (IDE), VESA Local Bus (VLB),Peripheral Component Interconnect (PCI), Card Bus, Universal Serial Bus(USB), Advanced Graphics Port (AGP), Firewire, and Small ComputerSystems Interface (SCSI). The system memory 1016 can also includevolatile memory 1020 and nonvolatile memory 1022. The basic input/outputsystem (BIOS), containing the basic routines to transfer informationbetween elements within the computer 1012, such as during start-up, canbe stored in nonvolatile memory 1022. By way of illustration, and notlimitation, nonvolatile memory 1022 can include read only memory (ROM),programmable ROM (PROM), electrically programmable ROM (EPROM),electrically erasable programmable ROM (EEPROM), flash memory, ornonvolatile random access memory (RAM) (e.g., ferroelectric RAM (FeRAM).Volatile memory 1020 can also include random access memory (RAM), whichacts as external cache memory. By way of illustration and notlimitation, RAM is available in many forms such as static RAM (SRAM),dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM(DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), directRambus RAM (DRRAM), direct Rambus dynamic RAM (DRDRAM), and Rambusdynamic RAM.

Computer 1012 can also include removable/non-removable,volatile/non-volatile computer storage media. FIG. 10 illustrates, forexample, a disk storage 1024. Disk storage 1024 can also include, but isnot limited to, devices like a magnetic disk drive, floppy disk drive,tape drive, Jaz drive, Zip drive, LS-100 drive, flash memory card, ormemory stick. The disk storage 1024 also can include storage mediaseparately or in combination with other storage media including, but notlimited to, an optical disk drive such as a compact disk ROM device(CD-ROM), CD recordable drive (CD-R Drive), CD rewritable drive (CD-RWDrive) or a digital versatile disk ROM drive (DVD-ROM). To facilitateconnection of the disk storage 1024 to the system bus 1018, a removableor non-removable interface can be used, such as interface 1026. FIG. 10also depicts software that can act as an intermediary between users andthe basic computer resources described in the suitable operatingenvironment 1000. Such software can also include, for example, anoperating system 1028. Operating system 1028, which can be stored ondisk storage 1024, acts to control and allocate resources of thecomputer 1012. System applications 1030 can take advantage of themanagement of resources by operating system 1028 through program modules1032 and program data 1034, e.g., stored either in system memory 1016 oron disk storage 1024. It is to be appreciated that this disclosure canbe implemented with various operating systems or combinations ofoperating systems. A user enters commands or information into thecomputer 1012 through one or more input devices 1036. Input devices 1036can include, but are not limited to, a pointing device such as a mouse,trackball, stylus, touch pad, keyboard, microphone, joystick, game pad,satellite dish, scanner, TV tuner card, digital camera, digital videocamera, web camera, and the like. These and other input devices canconnect to the processing unit 1014 through the system bus 1018 via oneor more interface ports 1038. The one or more Interface ports 1038 caninclude, for example, a serial port, a parallel port, a game port, and auniversal serial bus (USB). One or more output devices 1040 can use someof the same type of ports as input device 1036. Thus, for example, a USBport can be used to provide input to computer 1012, and to outputinformation from computer 1012 to an output device 1040. Output adapter1042 can be provided to illustrate that there are some output devices1040 like monitors, speakers, and printers, among other output devices1040, which require special adapters. The output adapters 1042 caninclude, by way of illustration and not limitation, video and soundcards that provide a means of connection between the output device 1040and the system bus 1018. It should be noted that other devices and/orsystems of devices provide both input and output capabilities such asone or more remote computers 1044.

Computer 1012 can operate in a networked environment using logicalconnections to one or more remote computers, such as remote computer1044. The remote computer 1044 can be a computer, a server, a router, anetwork PC, a workstation, a microprocessor based appliance, a peerdevice or other common network node and the like, and typically can alsoinclude many or all of the elements described relative to computer 1012.For purposes of brevity, only a memory storage device 1046 isillustrated with remote computer 1044. Remote computer 1044 can belogically connected to computer 1012 through a network interface 1048and then physically connected via communication connection 1050.Further, operation can be distributed across multiple (local and remote)systems. Network interface 1048 can encompass wire and/or wirelesscommunication networks such as local-area networks (LAN), wide-areanetworks (WAN), cellular networks, etc. LAN technologies include FiberDistributed Data Interface (FDDI), Copper Distributed Data Interface(CDDI), Ethernet, Token Ring and the like. WAN technologies include, butare not limited to, point-to-point links, circuit switching networkslike Integrated Services Digital Networks (ISDN) and variations thereon,packet switching networks, and Digital Subscriber Lines (DSL). One ormore communication connections 1050 refers to the hardware/softwareemployed to connect the network interface 1048 to the system bus 1018.While communication connection 1050 is shown for illustrative clarityinside computer 1012, it can also be external to computer 1012. Thehardware/software for connection to the network interface 1048 can alsoinclude, for exemplary purposes only, internal and external technologiessuch as, modems including regular telephone grade modems, cable modemsand DSL modems, ISDN adapters, and Ethernet cards.

Embodiments of the present invention can be a system, a method, anapparatus and/or a computer program product at any possible technicaldetail level of integration. The computer program product can include acomputer readable storage medium (or media) having computer readableprogram instructions thereon for causing a processor to carry outaspects of the present invention. The computer readable storage mediumcan be a tangible device that can retain and store instructions for useby an instruction execution device. The computer readable storage mediumcan be, for example, but is not limited to, an electronic storagedevice, a magnetic storage device, an optical storage device, anelectromagnetic storage device, a semiconductor storage device, or anysuitable combination of the foregoing. A non-exhaustive list of morespecific examples of the computer readable storage medium can alsoinclude the following: a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), a static randomaccess memory (SRAM), a portable compact disc read-only memory (CD-ROM),a digital versatile disk (DVD), a memory stick, a floppy disk, amechanically encoded device such as punch-cards or raised structures ina groove having instructions recorded thereon, and any suitablecombination of the foregoing. A computer readable storage medium, asused herein, is not to be construed as being transitory signals per se,such as radio waves or other freely propagating electromagnetic waves,electromagnetic waves propagating through a waveguide or othertransmission media (e.g., light pulses passing through a fiber-opticcable), or electrical signals transmitted through a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network can includecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device. Computer readable programinstructions for carrying out operations of various aspects of thepresent invention can be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions can executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer can be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection can be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) can execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to customize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions. These computer readable programinstructions can be provided to a processor of a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor of the computer or other programmabledata processing apparatus, create means for implementing thefunctions/acts specified in the flowchart and/or block diagram block orblocks. These computer readable program instructions can also be storedin a computer readable storage medium that can direct a computer, aprogrammable data processing apparatus, and/or other devices to functionin a particular manner, such that the computer readable storage mediumhaving instructions stored therein includes an article of manufactureincluding instructions which implement aspects of the function/actspecified in the flowchart and/or block diagram block or blocks. Thecomputer readable program instructions can also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational acts to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams can represent a module, segment, or portionof instructions, which includes one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks can occur out of theorder noted in the Figures. For example, two blocks shown in successioncan, in fact, be executed substantially concurrently, or the blocks cansometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

While the subject matter has been described above in the general contextof computer-executable instructions of a computer program product thatruns on a computer and/or computers, those skilled in the art willrecognize that this disclosure also can or can be implemented incombination with other program modules. Generally, program modulesinclude routines, programs, components, data structures, etc. thatperform particular tasks and/or implement particular abstract datatypes. Moreover, those skilled in the art will appreciate that theinventive computer-implemented methods can be practiced with othercomputer system configurations, including single-processor ormultiprocessor computer systems, mini-computing devices, mainframecomputers, as well as computers, hand-held computing devices (e.g., PDA,phone), microprocessor-based or programmable consumer or industrialelectronics, and the like. The illustrated aspects can also be practicedin distributed computing environments where tasks are performed byremote processing devices that are linked through a communicationsnetwork. However, some, if not all aspects of this disclosure can bepracticed on stand-alone computers. In a distributed computingenvironment, program modules can be located in both local and remotememory storage devices.

As used in this application, the terms “component,” “system,”“platform,” “interface,” and the like, can refer to and/or can include acomputer-related entity or an entity related to an operational machinewith one or more specific functionalities. The entities disclosed hereincan be either hardware, a combination of hardware and software,software, or software in execution. For example, a component can be, butis not limited to being, a process running on a processor, a processor,an object, an executable, a thread of execution, a program, and/or acomputer. By way of illustration, both an application running on aserver and the server can be a component. One or more components canreside within a process and/or thread of execution and a component canbe localized on one computer and/or distributed between two or morecomputers. In another example, respective components can execute fromvarious computer readable media having various data structures storedthereon. The components can communicate via local and/or remoteprocesses such as in accordance with a signal having one or more datapackets (e.g., data from one component interacting with anothercomponent in a local system, distributed system, and/or across a networksuch as the Internet with other systems via the signal). As anotherexample, a component can be an apparatus with specific functionalityprovided by mechanical parts operated by electric or electroniccircuitry, which is operated by a software or firmware applicationexecuted by a processor. In such a case, the processor can be internalor external to the apparatus and can execute at least a part of thesoftware or firmware application. As yet another example, a componentcan be an apparatus that provides specific functionality throughelectronic components without mechanical parts, wherein the electroniccomponents can include a processor or other means to execute software orfirmware that confers at least in part the functionality of theelectronic components. In an aspect, a component can emulate anelectronic component via a virtual machine, e.g., within a cloudcomputing system.

In addition, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.” That is, unless specified otherwise, or clearfrom context, “X employs A or B” is intended to mean any of the naturalinclusive permutations. That is, if X employs A; X employs B; or Xemploys both A and B, then “X employs A or B” is satisfied under any ofthe foregoing instances. Moreover, articles “a” and “an” as used in thesubject specification and annexed drawings should generally be construedto mean “one or more” unless specified otherwise or clear from contextto be directed to a singular form. As used herein, the terms “example”and/or “exemplary” are utilized to mean serving as an example, instance,or illustration. For the avoidance of doubt, the subject matterdisclosed herein is not limited by such examples. In addition, anyaspect or design described herein as an “example” and/or “exemplary” isnot necessarily to be construed as preferred or advantageous over otheraspects or designs, nor is it meant to preclude equivalent exemplarystructures and techniques known to those of ordinary skill in the art.

As it is employed in the subject specification, the term “processor” canrefer to substantially any computing processing unit or deviceincluding, but not limited to, single-core processors; single-processorswith software multithread execution capability; multi-core processors;multi-core processors with software multithread execution capability;multi-core processors with hardware multithread technology; parallelplatforms; and parallel platforms with distributed shared memory.Additionally, a processor can refer to an integrated circuit, anapplication specific integrated circuit (ASIC), a digital signalprocessor (DSP), a field programmable gate array (FPGA), a programmablelogic controller (PLC), a complex programmable logic device (CPLD), adiscrete gate or transistor logic, discrete hardware components, or anycombination thereof designed to perform the functions described herein.Further, processors can exploit nano-scale architectures such as, butnot limited to, molecular and quantum-dot based transistors, switchesand gates, in order to optimize space usage or enhance performance ofuser equipment. A processor can also be implemented as a combination ofcomputing processing units. In this disclosure, terms such as “store,”“storage,” “data store,” data storage,” “database,” and substantiallyany other information storage component relevant to operation andfunctionality of a component are utilized to refer to “memorycomponents,” entities embodied in a “memory,” or components including amemory. It is to be appreciated that memory and/or memory componentsdescribed herein can be either volatile memory or nonvolatile memory, orcan include both volatile and nonvolatile memory. By way ofillustration, and not limitation, nonvolatile memory can include readonly memory (ROM), programmable ROM (PROM), electrically programmableROM (EPROM), electrically erasable ROM (EEPROM), flash memory, ornonvolatile random access memory (RAM) (e.g., ferroelectric RAM (FeRAM).Volatile memory can include RAM, which can act as external cache memory,for example. By way of illustration and not limitation, RAM is availablein many forms such as synchronous RAM (SRAM), dynamic RAM (DRAM),synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhancedSDRAM (ESDRAM), Synchlink DRAM (SLDRAM), direct Rambus RAM (DRRAM),direct Rambus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM).Additionally, the disclosed memory components of systems orcomputer-implemented methods herein are intended to include, withoutbeing limited to including, these and any other suitable types ofmemory.

What has been described above include mere examples of systems, computerprogram products and computer-implemented methods. It is, of course, notpossible to describe every conceivable combination of components,products and/or computer-implemented methods for purposes of describingthis disclosure, but one of ordinary skill in the art can recognize thatmany further combinations and permutations of this disclosure arepossible. Furthermore, to the extent that the terms “includes,” “has,”“possesses,” and the like are used in the detailed description, claims,appendices and drawings such terms are intended to be inclusive in amanner similar to the term “comprising” as “comprising” is interpretedwhen employed as a transitional word in a claim. The descriptions of thevarious embodiments have been presented for purposes of illustration,but are not intended to be exhaustive or limited to the embodimentsdisclosed. Many modifications and variations will be apparent to thoseof ordinary skill in the art without departing from the scope and spiritof the described embodiments. The terminology used herein was chosen tobest explain the principles of the embodiments, the practicalapplication or technical improvement over technologies found in themarketplace, or to enable others of ordinary skill in the art tounderstand the embodiments disclosed herein.

What is claimed is:
 1. A system, comprising: a memory that storescomputer executable components; a processor, operably coupled to thememory, and that executes the computer executable components stored inthe memory, wherein the computer executable components comprise: anassessment component that determines a susceptibility disposition valuethat measures a susceptibility of an entity to a treatment service basedon a trust disposition value, wherein the trust disposition value isdetermined based on a communication with the entity using machinelearning technology.
 2. The system of claim 1, further comprising: adata collection component that generates a dataset that represents aplurality of features extracted from the communication; and anevaluation component that determines the trust disposition valueassociated with the entity by analyzing the dataset using the machinelearning technology.
 3. The system of claim 2, wherein the communicationis between the entity and an artificial intelligence system.
 4. Thesystem of claim 2, wherein the evaluation component further determinesthe trust disposition value based on a trust graph, and wherein thetrust graph represents a plurality of trust dispositions associated witha plurality of entities.
 5. The system of claim 2, wherein thecommunication regards a commitment regarding the entity, and wherein theplurality of features delineates whether the commitment was fulfilled.6. The system of claim 1, further comprising: a distribution componentthat generates a recommendation regarding whether to perform thetreatment service based on the susceptibility disposition value, andwherein the treatment service regards a chemical effect affecting theentity.
 7. The system of claim 6, wherein the chemical effect is achemically induced dependency generated by a chemical compound.
 8. Thesystem of claim 6, wherein the distribution component sends therecommendation to the entity.
 9. The system of claim 1, furthercomprising: a distribution component that distributes the treatmentservice to the entity based on a comparison of the susceptibilitydisposition value to a predefined threshold.
 10. The system of claim 1,wherein the susceptibility disposition value is determined in a cloudcomputing environment.
 11. A computer-implemented method, comprising:determining, by a system operatively coupled to a processor, asusceptibility disposition value that measures a susceptibility of anentity to a treatment service based on a trust disposition value,wherein the trust disposition value is determined based on acommunication with the entity using machine learning technology.
 12. Thecomputer-implemented method of claim 11, further comprising: generating,by the system, a dataset that represents a plurality of featuresextracted from the communication regarding the entity; and determining,by the system, the trust disposition value associated with the entity byanalyzing the dataset using the machine learning technology.
 13. Thecomputer-implemented method of claim 11, further comprising: generating,by the system, a recommendation regarding whether to perform thetreatment service based on the susceptibility disposition value, andwherein the treatment service regards a chemical effect affecting theentity.
 14. The computer-implemented method of claim 11, furthercomprising: distributing, by the system, the treatment service to theentity based on a comparison of the susceptibility disposition value toa predefined threshold value.
 15. The computer-implemented method ofclaim 14, wherein the treatment service is a media selected from a groupconsisting of a text, a video and an audio signal.
 16. A computerprogram product for assessing a treatment service, the computer programproduct comprising a computer readable storage medium having programinstructions embodied therewith, the program instructions executable bya processor to cause the processor to: determine, by a systemoperatively coupled to the processor, a susceptibility disposition valuethat measures a susceptibility of an entity to the treatment servicebased on a trust disposition value, wherein the trust disposition valueis determined based on a communication with the entity using machinelearning technology.
 17. The computer program product of claim 16,wherein the program instructions further cause the processor to:generate, by the system, a dataset that represents a plurality offeatures extracted from the communication regarding the entity; anddetermine, by the system, the trust disposition value associated withthe entity by analyzing the dataset using the machine learningtechnology.
 18. The computer program product of claim 17, wherein thecommunication is between the entity and an artificial intelligencesystem.
 19. The computer program product of claim 18, wherein theprogram instructions further cause the processor to: generate, by thesystem, a recommendation regarding whether to perform the treatmentservice based on the susceptibility disposition value, and wherein thetreatment service regards a chemical effect affecting the entity. 20.The computer program product of claim 18, wherein the trust dispositionvalue is determined in a cloud computing environment.